Cross Pseudo-Labeling for Semi-Supervised Audio-Visual Source Localization

• URL: http://arxiv.org/abs/2403.03095v1
• Date: Tue, 5 Mar 2024 16:28:48 GMT
• Title: Cross Pseudo-Labeling for Semi-Supervised Audio-Visual Source Localization
• Authors: Yuxin Guo, Shijie Ma, Yuhao Zhao, Hu Su, Wei Zou
• Abstract summary: We propose a novel method named Cross Pseudo-Labeling (XPL), wherein two models learn from each other with the cross-refine mechanism to avoid bias accumulation. XPL significantly outperforms existing methods, achieving state-of-the-art performance while effectively mitigating confirmation bias.
• Score: 9.791311361007397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Audio-Visual Source Localization (AVSL) is the task of identifying specific sounding objects in the scene given audio cues. In our work, we focus on semi-supervised AVSL with pseudo-labeling. To address the issues with vanilla hard pseudo-labels including bias accumulation, noise sensitivity, and instability, we propose a novel method named Cross Pseudo-Labeling (XPL), wherein two models learn from each other with the cross-refine mechanism to avoid bias accumulation. We equip XPL with two effective components. Firstly, the soft pseudo-labels with sharpening and pseudo-label exponential moving average mechanisms enable models to achieve gradual self-improvement and ensure stable training. Secondly, the curriculum data selection module adaptively selects pseudo-labels with high quality during training to mitigate potential bias. Experimental results demonstrate that XPL significantly outperforms existing methods, achieving state-of-the-art performance while effectively mitigating confirmation bias and ensuring training stability.

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